OpenRaiser/Intern-Atlas huggingface dataset:
A Survey on Open Information Extraction from Rule-based Model to Large Language Model
Venue: arXiv cs.CL 2024
Year: 2024

Open Information Extraction (OpenIE) represents a crucial NLP task aimed at deriving structured information from unstructured text, unrestricted by relation type or domain. This survey paper provides an overview of OpenIE technologies spanning from 2007 to 2024, emphasizing a chronological perspective absent in prior surveys. It examines the evolution of task settings in OpenIE to align with the advances in recent technologies. The paper categorizes OpenIE approaches into rule-based, neural, and pre-trained large language models, discussing each within a chronological framework. Additionally, it highlights prevalent datasets and evaluation metrics currently in use. Building on this extensive review, the paper outlines potential future directions in terms of datasets, information sources, output formats, methodologies, and evaluation metrics.
Walking on Words
Venue: arXiv cs.DM 2024
Year: 2024

Take any word over some alphabet. If it is non-empty, go to any position and print out the letter being scanned. Now repeat the following any number of times (possibly zero): either stay at the current letter, or move one letter leftwards (if possible) or move one letter rightwards (if possible); then print out the letter being scanned. In effect, we are going for a walk on the input word. Let u be the infix of the input word comprising the visited positions, and w the word printed out (empty if the input word is). Since any unvisited prefix or suffix of the input word cannot influence w, we may as well discard them, and say that u generates w. We ask: given a word w, what words u generate it? The answer is surprising. Call u a primitive generator of w if u generates w and is not generated by any word shorter than u. We show that, excepting some degenerate cases, every word has precisely two primitive generators.
Causality-Inspired Taxonomy for Explainable Artificial Intelligence
Venue: arXiv cs.CV 2024
Year: 2024

As two sides of the same coin, causality and explainable artificial intelligence (xAI) were initially proposed and developed with different goals. However, the latter can only be complete when seen through the lens of the causality framework. As such, we propose a novel causality-inspired framework for xAI that creates an environment for the development of xAI approaches. To show its applicability, biometrics was used as case study. For this, we have analysed 81 research papers on a myriad of biometric modalities and different tasks. We have categorised each of these methods according to our novel xAI Ladder and discussed the future directions of the field.
Is Medieval Distant Viewing Possible? : Extending and Enriching Annotation of Legacy Image Collections using Visual Analytics
Venue: arXiv cs.CV 2024
Year: 2024

Distant viewing approaches have typically used image datasets close to the contemporary image data used to train machine learning models. To work with images from other historical periods requires expert annotated data, and the quality of labels is crucial for the quality of results. Especially when working with cultural heritage collections that contain myriad uncertainties, annotating data, or re-annotating, legacy data is an arduous task. In this paper, we describe working with two pre-annotated sets of medieval manuscript images that exhibit conflicting and overlapping metadata. Since a manual reconciliation of the two legacy ontologies would be very expensive, we aim (1) to create a more uniform set of descriptive labels to serve as a "bridge" in the combined dataset, and (2) to establish a high quality hierarchical classification that can be used as a valuable input for subsequent supervised machine learning. To achieve these goals, we developed visualization and interaction mechanisms, enabling medievalists to combine, regularize and extend the vocabulary used to describe these, and other cognate, image datasets. The visual interfaces provide experts an overview of relationships in the data going beyond the sum total of the metadata. Word and image embeddings as well as co-occurrences of labels across the datasets, enable batch re-annotation of images, recommendation of label candidates and support composing a hierarchical classification of labels.
BSpell: A CNN-Blended BERT Based Bangla Spell Checker
Venue: arXiv cs.CL 2024
Year: 2024

Bangla typing is mostly performed using English keyboard and can be highly erroneous due to the presence of compound and similarly pronounced letters. Spelling correction of a misspelled word requires understanding of word typing pattern as well as the context of the word usage. A specialized BERT model named BSpell has been proposed in this paper targeted towards word for word correction in sentence level. BSpell contains an end-to-end trainable CNN sub-model named SemanticNet along with specialized auxiliary loss. This allows BSpell to specialize in highly inflected Bangla vocabulary in the presence of spelling errors. Furthermore, a hybrid pretraining scheme has been proposed for BSpell that combines word level and character level masking. Comparison on two Bangla and one Hindi spelling correction dataset shows the superiority of our proposed approach. BSpell is available as a Bangla spell checking tool via GitHub: https://github.com/Hasiburshanto/Bangla-Spell-Checker
Analyzing Trends in Tor
Venue: arXiv cs.CR 2024
Year: 2024

The Tor Network has been a significant part of the Internet for years. Tor was originally started in the Naval Research Laboratory for anonymous Internet browsing and Internet-based communication. From being used for anonymous communications, it has now segmented into various other use-cases like censorship circumvention, performing illegal activities, etc. In this paper, we perform empirical measurements on the Tor network to analyze the trends in Tor over the years. We gather our measurements data through our measurement scripts, past research in this domain, and aggregated data provided by the Tor metrics directory. We use this data to analyze trends and understand the incidents that caused fluctuations in the trends of different data parameters. We collect measurements data for Tor parameters like Tor users, onion services, Tor relays, and bridges, etc. We also study censorshiprelated events and study trends by analyzing censorship-related metrics. Finally, we touch upon the location diversity in Tor and study how the Tor circuit selection and construction are impacted by the bandwidth distribution of Tor relays across geographies.
New research paradigms and agenda of human factors science in the intelligence era
Venue: arXiv cs.HC 2024
Year: 2024

This paper proposes the innovative concept of "human factors science" to characterize engineering psychology, human factors engineering, human-computer interaction, and other similar fields. Although the perspectives in these fields differ, they share a common approach: "human-centered design." In the AI era, the human-machine relationship presents a trans-era evolution to "human-AI teaming." The change has raised challenges for human factors science, compelling us to re-examine current research paradigms and agendas. Based on our previous work, this paper proposes three research paradigms: (1) human-AI joint cognitive systems: this regards an intelligent agent as a cognitive agent with a certain level of cognitive capabilities. A human-AI system can be characterized as a joint cognitive system in which humans and intelligent agents work as teammates for collaboration; (2) human-AI joint cognitive ecosystems: an intelligent ecosystem with multiple human-AI systems can be represented as a human-AI joint cognitive ecosystem. The overall performance of the ecosystem depends on optima collaboration and design across the multiple human-AI systems; (3) intelligent sociotechnical systems (iSTS): human-AI systems are design, developed, and deployed in an iSTS environment. The successful design, development, and deployment of a human-AI system within an iSTS environment depends on the synergistic optimization between the subsystems. This paper looks forward to the future research agenda of human factors science from three aspects: human-AI interaction, intelligent human-machine interface, and human-AI teaming. Analyses show that the three new research paradigms will benefit future research in human factors science. We believe the proposed research paradigms and the future research agenda will mutually promote each other, further advancing human factors science in the AI era.
Neuromorphic Visual Scene Understanding with Resonator Networks
Venue: arXiv cs.CV 2024
Year: 2024

Analyzing a visual scene by inferring the configuration of a generative model is widely considered the most flexible and generalizable approach to scene understanding. Yet, one major problem is the computational challenge of the inference procedure, involving a combinatorial search across object identities and poses. Here we propose a neuromorphic solution exploiting three key concepts: (1) a computational framework based on Vector Symbolic Architectures (VSA) with complex-valued vectors; (2) the design of Hierarchical Resonator Networks (HRN) to factorize the non-commutative transforms translation and rotation in visual scenes; (3) the design of a multi-compartment spiking phasor neuron model for implementing complex-valued resonator networks on neuromorphic hardware. The VSA framework uses vector binding operations to form a generative image model in which binding acts as the equivariant operation for geometric transformations. A scene can, therefore, be described as a sum of vector products, which can then be efficiently factorized by a resonator network to infer objects and their poses. The HRN features a partitioned architecture in which vector binding is equivariant for horizontal and vertical translation within one partition and for rotation and scaling within the other partition. The spiking neuron model allows mapping the resonator network onto efficient and low-power neuromorphic hardware. Our approach is demonstrated on synthetic scenes composed of simple 2D shapes undergoing rigid geometric transformations and color changes. A companion paper demonstrates the same approach in real-world application scenarios for machine vision and robotics.
Learning Informative Health Indicators Through Unsupervised Contrastive Learning
Venue: arXiv cs.LG 2024
Year: 2024

Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health status of industrial assets over time serve as valuable tools for e.g. fault detection or prognostics. This study proposes a novel, versatile and unsupervised approach to learn health indicators using contrastive learning, where the operational time serves as a proxy for degradation. To highlight its versatility, the approach is evaluated on two tasks and case studies with different characteristics: wear assessment of milling machines and fault detection of railway wheels. Our results show that the proposed methodology effectively learns a health indicator that follows the wear of milling machines (0.97 correlation on average) and is suitable for fault detection in railway wheels (88.7% balanced accuracy). The conducted experiments demonstrate the versatility of the approach for various systems and health conditions.
ANAct: Adaptive Normalization for Activation Functions
Venue: arXiv cs.LG 2024
Year: 2024

In this paper, we investigate the negative effect of activation functions on forward and backward propagation and how to counteract this effect. First, We examine how activation functions affect the forward and backward propagation of neural networks and derive a general form for gradient variance that extends the previous work in this area. We try to use mini-batch statistics to dynamically update the normalization factor to ensure the normalization property throughout the training process, rather than only accounting for the state of the neural network after weight initialization. Second, we propose ANAct, a method that normalizes activation functions to maintain consistent gradient variance across layers and demonstrate its effectiveness through experiments. We observe that the convergence rate is roughly related to the normalization property. We compare ANAct with several common activation functions on CNNs and residual networks and show that ANAct consistently improves their performance. For instance, normalized Swish achieves 1.4\% higher top-1 accuracy than vanilla Swish on ResNet50 with the Tiny ImageNet dataset and more than 1.2\% higher with CIFAR-100.
Coded Caching with Shared Caches and Private Caches
Venue: arXiv cs.IT 2024
Year: 2024

This work studies the coded caching problem in a setting where the users are simultaneously endowed with a private cache and a shared cache. The setting consists of a server connected to a set of users, assisted by a smaller number of helper nodes that are equipped with their own storage. In addition to the helper cache, each user possesses a dedicated cache which is also used to prefetch file contents. Each helper cache can serve an arbitrary number of users, but each user gets served by only one helper cache. We consider two scenarios: (a) the server has no prior information about the user-to-helper cache association, and (b) the server knows the user-to-helper cache association at the placement phase itself. We design centralized coded caching schemes under uncoded placement for the above two settings. For case (b), two schemes are proposed that are optimal in certain memory regimes. Further, a cut-set based lower bound is derived and used to show that one of the proposed schemes for case (b) is optimal in certain memory regime.
Information Visualization for Effective Altruism
Venue: arXiv cs.HC 2024
Year: 2024

Effective altruism is a movement whose goal it to use evidence and reason to figure out how to benefit others as much as possible. This movement is becoming influential, but effective altruists still lack tools to help them understand complex humanitarian trade-offs and make good decisions based on data. Visualization-the study of computer-supported, visual representations of data meant to support understanding, communication, and decision makingcan help alleviate this issue. Conversely, effective altruism provides a powerful thinking framework for visualization research that focuses on humanitarian applications.
Detection of diabetic retinopathy using longitudinal self-supervised learning
Venue: arXiv cs.CV 2024
Year: 2024

Longitudinal imaging is able to capture both static anatomical structures and dynamic changes in disease progression towards earlier and better patient-specific pathology management. However, conventional approaches for detecting diabetic retinopathy (DR) rarely take advantage of longitudinal information to improve DR analysis. In this work, we investigate the benefit of exploiting self-supervised learning with a longitudinal nature for DR diagnosis purposes. We compare different longitudinal self-supervised learning (LSSL) methods to model the disease progression from longitudinal retinal color fundus photographs (CFP) to detect early DR severity changes using a pair of consecutive exams. The experiments were conducted on a longitudinal DR screening dataset with or without those trained encoders (LSSL) acting as a longitudinal pretext task. Results achieve an AUC of 0.875 for the baseline (model trained from scratch) and an AUC of 0.96 (95% CI: 0.9593-0.9655 DeLong test) with a p-value < 2.2e-16 on early fusion using a simple ResNet alike architecture with frozen LSSL weights, suggesting that the LSSL latent space enables to encode the dynamic of DR progression.
When Robotics Meets Wireless Communications: An Introductory Tutorial
Venue: arXiv cs.RO 2024
Year: 2024

The importance of ground Mobile Robots (MRs) and Unmanned Aerial Vehicles (UAVs) within the research community, industry, and society is growing fast. Many of these agents are nowadays equipped with communication systems that are, in some cases, essential to successfully achieve certain tasks. In this context, we have begun to witness the development of a new interdisciplinary research field at the intersection of robotics and communications. This research field has been boosted by the intention of integrating UAVs within the 5G and 6G communication networks. This research will undoubtedly lead to many important applications in the near future. Nevertheless, one of the main obstacles to the development of this research area is that most researchers address these problems by oversimplifying either the robotics or the communications aspect. This impedes the ability of reaching the full potential of this new interdisciplinary research area. In this tutorial, we present some of the modelling tools necessary to address problems involving both robotics and communication from an interdisciplinary perspective. As an illustrative example of such problems, we focus in this tutorial on the issue of communication-aware trajectory planning.
Multi-class Classifier based Failure Prediction with Artificial and Anonymous Training for Data Privacy
Venue: arXiv cs.AI 2024
Year: 2024

This paper proposes a novel non-intrusive system failure prediction technique using available information from developers and minimal information from raw logs (rather than mining entire logs) but keeping the data entirely private with the data owners. A neural network based multi-class classifier is developed for failure prediction, using artificially generated anonymous data set, applying a combination of techniques, viz., genetic algorithm (steps), pattern repetition, etc., to train and test the network. The proposed mechanism completely decouples the data set used for training process from the actual data which is kept private. Moreover, multi-criteria decision making (MCDM) schemes are used to prioritize failures meeting business requirements. Results show high accuracy in failure prediction under different parameter configurations. On a broader context, any classification problem, beyond failure prediction, can be performed using the proposed mechanism with artificially generated data set without looking into the actual data as long as the input features can be translated to binary values (e.g. output from private binary classifiers) and can provide classification-as-a-service.
Passive Realizations of Series Elastic Actuation: Effects of Plant and Controller Dynamics on Haptic Rendering Performance
Venue: arXiv cs.RO 2024
Year: 2024

We introduce minimal passive physical equivalents of series (damped) elastic actuation (S(D)EA) under closed-loop control to determine the effect of different plant parameters and controller gains on the closed-loop performance of the system and to help establish an intuitive understanding of the passivity bounds. Furthermore, we explicitly derive the feasibility conditions for these passive physical equivalents and compare them to the necessary and sufficient conditions for the passivity of S(D)EA under velocity sourced impedance control (VSIC) to establish their relationship. Through the passive physical equivalents, we rigorously compare the effect of different plant dynamics (e.g., SEA and SDEA) on the system performance. We demonstrate that passive physical equivalents make the effect of controller gains explicit and establish a natural means for effective impedance analysis. We also show that passive physical equivalents promote co-design thinking by enforcing simultaneous and unbiased consideration of (possibly negative) controller gains and plant parameters. We demonstrate the usefulness of negative controller gains when coupled to properly designed plant dynamics. Finally, we provide experimental validations of our theoretical results and characterizations of the haptic rendering performance of S(D)EA under VSIC.
From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent
Venue: arXiv cs.AI 2024
Year: 2024

The goal of Explainable AI (XAI) is to design methods to provide insights into the reasoning process of black-box models, such as deep neural networks, in order to explain them to humans. Social science research states that such explanations should be conversational, similar to human-to-human explanations. In this work, we show how to incorporate XAI in a conversational agent, using a standard design for the agent comprising natural language understanding and generation components. We build upon an XAI question bank, which we extend by quality-controlled paraphrases, to understand the user's information needs. We further systematically survey the literature for suitable explanation methods that provide the information to answer those questions, and present a comprehensive list of suggestions. Our work is the first step towards truly natural conversations about machine learning models with an explanation agent. The comprehensive list of XAI questions and the corresponding explanation methods may support other researchers in providing the necessary information to address users' demands. To facilitate future work, we release our source code and data.
A Subexponential Quantum Algorithm for the Semidirect Discrete Logarithm Problem
Venue: arXiv cs.CR 2024
Year: 2024

Group-based cryptography is a relatively unexplored family in post-quantum cryptography, and the so-called Semidirect Discrete Logarithm Problem (SDLP) is one of its most central problems. However, the complexity of SDLP and its relationship to more well-known hardness problems, particularly with respect to its security against quantum adversaries, has not been well understood and was a significant open problem for researchers in this area. In this paper we give the first dedicated security analysis of SDLP. In particular, we provide a connection between SDLP and group actions, a context in which quantum subexponential algorithms are known to apply. We are therefore able to construct a subexponential quantum algorithm for solving SDLP, thereby classifying the complexity of SDLP and its relation to known computational problems.
Predicting the Next Action by Modeling the Abstract Goal
Venue: arXiv cs.CV 2024
Year: 2024

The problem of anticipating human actions is an inherently uncertain one. However, we can reduce this uncertainty if we have a sense of the goal that the actor is trying to achieve. Here, we present an action anticipation model that leverages goal information for the purpose of reducing the uncertainty in future predictions. Since we do not possess goal information or the observed actions during inference, we resort to visual representation to encapsulate information about both actions and goals. Through this, we derive a novel concept called abstract goal which is conditioned on observed sequences of visual features for action anticipation. We design the abstract goal as a distribution whose parameters are estimated using a variational recurrent network. We sample multiple candidates for the next action and introduce a goal consistency measure to determine the best candidate that follows from the abstract goal. Our method obtains impressive results on the very challenging Epic-Kitchens55 (EK55), EK100, and EGTEA Gaze+ datasets. We obtain absolute improvements of +13.69, +11.24, and +5.19 for Top-1 verb, Top-1 noun, and Top-1 action anticipation accuracy respectively over prior state-of-the-art methods for seen kitchens (S1) of EK55. Similarly, we also obtain significant improvements in the unseen kitchens (S2) set for Top-1 verb (+10.75), noun (+5.84) and action (+2.87) anticipation. Similar trend is observed for EGTEA Gaze+ dataset, where absolute improvement of +9.9, +13.1 and +6.8 is obtained for noun, verb, and action anticipation. It is through the submission of this paper that our method is currently the new state-of-the-art for action anticipation in EK55 and EGTEA Gaze+ https://competitions.codalab.org/competitions/20071#results Code available at https://github.com/debadityaroy/Abstract_Goal